AI Enablement, Governance & Automation for GxP Teams

AI is changing how GxP work gets done. The question is whether you’re guiding that change — or reacting to it.

We help teams adopt AI only where it will succeed — assessing readiness, embedding governance-first controls, and delivering practical training. Wait too long and you fall behind. Move too fast without controls and you create findings. We help you find the right pace.

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Human judgment stays accountable. AI stays assistive.

At our core: Clarity. Control. Confidence. Integrity.

The AI Adoption Problem Facing Pharmaceutical and Pharmacovigilance Teams

Two ways to get this wrong — and both create avoidable operational and strategic risk during early AI adoption.

Do nothing

Manual workloads keep growing
Good people burn out on administrative work
Competitors who adopt properly move faster
When adoption eventually happens, it’s reactive and rushed

Adopt poorly

Unvalidated tools embedded into critical workflows
Spend committed to vendors that can’t deliver safely
“Black box” models no one can explain when challenged
Accountability gaps when AI-assisted decisions are questioned

The middle path requires expertise.

Regulatory understanding and practical AI experience. Most organisations have one or the other — rarely both.

What’s actually happening

You’re not imagining the pressure:

Vendors are embedding AI into platforms you already use — Veeva, ArisGlobal, Medidata
Competitors are piloting literature surveillance, case processing, and document drafting
Your own staff are using tools like ChatGPT — often without oversight or documentation
Regulatory expectations are forming — GAMP 5 D11, ISO 42001, TGA guidance emerging

The window for controlled adoption is now.

Early adopters build structure while guidance is still forming. Late adopters scramble under pressure.

AI Adoption and Training Services for Pharmaceutical & Pharmacovigilance Teams

AI Readiness Assessment

Where are you? What’s realistic? What foundations do you need?

Current-state review (systems, data quality, team capability)
Use-case identification based on your context
AI risk classification and impact scoping
Regulatory pathway mapping
Gap analysis and prioritised recommendations

What you get: Clear roadmap, prioritised use cases, realistic ROI projections, governance requirements

Vendor Evaluation

Independent assessment before you commit the budget.

Capability review against your requirements
GAMP 5 validation pathway analysis
Data integrity and audit trail assessment
Realistic ROI vs vendor claims
Risk register and recommendation report

What you get: Independent assessment, regulatory risk analysis, evidence-based recommendation report

Implementation Support

From use-case design through deployment.

Validation protocols (GAMP 5 aligned)
Governance documentation integrated with your QMS
Human oversight protocols and monitoring
Team training to explain and defend AI use

What you get: Validated AI workflow, governance SOPs, trained team, inspection-ready evidence

Ongoing Advisory

On-demand support as your AI portfolio evolves.

New use-case evaluation
Governance refinement as expectations evolve
Inspection preparation and response support
Ad-hoc, context-specific guidance

What you get: Ongoing expertise, regulatory intelligence, governance confidence

Training

Build confident, consistent AI use across the team—without creating chaos or “shadow” adoption.

Role-based AI literacy (executives, quality, PV, clinical, operations)
Safe prompting and documentation habits for regulated work
Use-case walkthroughs using your tools and templates
Practical guardrails: scope, decision rights, minimum evidence
Train-the-trainer to embed capability internally
Agents that work using Microsoft Copilot and other solutions.

What you get: Trained team, practical playbooks, consistent ways of working, reduced “shadow AI” risk

Automation & Agents (Built With Your Team)

Design and build lean automations and AI agents that reduce admin load—while maintaining oversight and evidence.

Identify one high-value workflow to pilot.
Build in your environment (M365/Copilot, approved platforms, existing tools)
Human-in-the-loop checkpoints for review, approval, and escalation
Logging, traceability, and version control for prompts and outputs
Handover so your team can maintain, extend, and govern.

What you get: Working automations and agents, documented operating model, measurable time savings.

AI Governance & Strategy

AI Integration & Workflow Systems

AI Enablement & Capability Building

AI Applications for GxP Teams Using Available Tools

Use Case	The Problem	What AI Does
Literature Surveillance Screening	Weekly PubMed alerts produce 80–150 abstracts; manual screening takes 4–6 hours and fatigue can lead to missed signals.	Screens abstracts against safety profile, classifies as Relevant/Monitor/Exclude, and flags items needing full-text review.
ICSR Case Narrative Drafting	Narratives take 30–60 minutes each; time is spent on structure and synthesis rather than clinical judgement.	Generates first-draft CIOMS-format narratives from structured data, flags missing information, and standardises language.
Meeting Minutes & Action Tracking	Minutes vary in quality and action items are missed or inconsistently captured across meetings.	Transcribes meetings, produces structured minutes, extracts actions with owners/deadlines, and flags decisions needing documentation.
SOP First-Draft Generation	SOP drafting takes 2–8 weeks; blank-page starts and inconsistent structure slow delivery.	Generates a structured first draft from templates and references, includes required sections, and flags company-specific inputs.
Training Material Development	Building training decks, job aids, and quizzes from SOPs takes days and materials lag after SOP updates.	Converts SOPs into training outlines, quiz questions with rationales, one-page job aids, and highlights critical steps.
Regulatory Intelligence Monitoring	Regulators publish frequent updates; manual monitoring misses changes and impact assessments become reactive.	Scans sources for relevant updates, summarises key changes, compares to procedures, and suggests impact priorities.
Audit Preparation Evidence Gathering	Preparation takes 80–100 hours; evidence is scattered and gap identification is manual.	Cross-references checklists to documentation, identifies gaps, drafts mock questions, and suggests supporting evidence.
CAPA Drafting & Root Cause Analysis	Drafting effective CAPAs is difficult; generic actions miss root cause and drive repeat findings.	Structures 5 Whys, identifies systemic factors, and drafts CAPAs with actions, evidence requirements, and effectiveness checks.
Document Consistency Checking	Large SOP suites contain inconsistent terminology, outdated cross-references, and conflicting procedures.	Compares documents for terminology and process conflicts, validates cross-references, and suggests consolidation opportunities.
Regulatory Submission Section Drafting	Initial drafts for clinical overviews, safety summaries, and modules take 8–16 hours per document.	Produces structured first drafts, synthesises multiple inputs, and maintains consistent regulatory language.
Email Thread Summarisation	Long correspondence threads bury context; handovers lose commitments and decision points.	Summarises threads, extracts commitments/deadlines, highlights open questions, and identifies key decisions.
Deviation Investigation Support	Investigations require context gathering, pattern checks against past events, and careful documentation at scale.	Structures investigation reports, surfaces similar deviations, suggests factors to consider, and drafts impact assessments.
Vendor Qualification Questionnaire Analysis	Vendor questionnaires take hours to review; manual comparison to requirements misses gaps and inconsistencies.	Reviews responses against criteria, flags gaps and inconsistencies, and produces a structured assessment summary.
Periodic Safety Report Preparation	PSUR/PBRER preparation requires synthesising multiple sources; narrative drafting takes days for many teams.	Drafts narrative sections, summarises case series, maintains consistent update language, and flags data gaps.
Inspection Response Drafting	Responses require careful language and consistent commitments; drafting and coordination can take days.	Drafts structured responses to each observation, frames corrective commitments, and maintains regulatory tone.

Common Questions and Answers

Do you implement AI tools or just advise?

We guide use-case definition, vendor selection, governance, validation, and training. You keep control of IT build and vendors; we act as your regulatory and assurance guide.

How long does it take to be “AI-ready”?

Readiness is typically 2–3 weeks, vendor reviews 2–3 weeks per solution, and validated GxP use cases often take 3–6 months end-to-end.

Will your framework stand up to TGA/FDA inspection?

Frameworks align with GAMP 5, ISO 42001, and current regulatory signals, but accountability for implementation and evidence always remains with you.

Can you work with tools we already use (Copilot, ChatGPT, Veeva, ArisGlobal, etc.)?

Yes. Work is platform-agnostic and focuses on defining intended use, validation approach, vendor controls, and QMS integration.

What if our team doesn’t understand AI?

That’s the starting point. Training is embedded into every engagement and tailored for leadership, operational teams, and specific use cases.

Do you offer training only?

Yes. Many organisations start with AI literacy and governance training before engaging vendors or implementing tools.

What industries do you work with?

Primarily GxP-regulated organisations: pharma, biotech, CROs, medical devices, IVDs, clinical labs, and regulated healthcare services.

Do you work outside Australia?

Yes. Clients are supported across Australia, New Zealand, and APAC, with frameworks aligned to local and global regulators.

Is AI right for us yet?

Readiness assessments determine whether AI will deliver value now or whether data, processes, or QMS foundations need strengthening first.

What regulatory frameworks apply to AI in GxP?

Key references include GAMP 5 (Appendix D11), ISO/IEC 42001, TGA/FDA guidance, EU AI Act, and PV/clinical safety recommendations.

Do we need to disclose AI use to patients or regulators?

Often yes. Expectations depend on context, but transparency around AI use is increasingly expected by regulators and ethics bodies.

Who is liable if AI makes a mistake?

Humans remain accountable. AI supports decisions; liability sits with sponsors, manufacturers, and clinicians based on how AI is designed and used.

Disclaimer

GxPVigilance provides vendor-neutral advisory, training, governance, and validation guidance to support responsible AI adoption in GxP-regulated environments. Our services are educational and advisory in nature and do not constitute legal, clinical, financial, or regulatory advice.

We do not develop or sell AI tools, make regulatory submissions, or guarantee regulatory outcomes, inspection results, efficiency gains, or third-party vendor performance. References to tools or platforms are provided for informational purposes only.

Responsibility for compliance, validation, data privacy and security, and human oversight of AI-assisted activities remains with your organisation. To the extent permitted by law, GxPVigilance’s liability is limited to fees paid for services, and all engagements are governed by the laws of New South Wales and Queensland, Australia.